Vol.:(0123456789) 1 3 Transactions on Electrical and Electronic Materials https://doi.org/10.1007/s42341-021-00312-5 REGULAR PAPER A Self‑adaptive Algorithm with Newton Raphson Method for Parameters Identifcation of Photovoltaic Modules and Array Patrick Juvet Gnetchejo 1  · Salomé Ndjakomo Essiane 1,2  · Abdouramani Dadjé 3  · Pierre Ele 1,4  · Daniel Eutyche Mbadjoun Wapet 4  · Steve Perabi Ngofe 1  · Zhicong Chen 5 Received: 23 July 2020 / Revised: 5 March 2021 / Accepted: 11 March 2021 © The Korean Institute of Electrical and Electronic Material Engineers 2021 Abstract The word’s demand for renewable energy has be rinsing incrementally. One of the solutions for the energy crisis is photovol- taic. However, the design and development of better performing photovoltaic cells and modules requires accurate extraction of their intrinsic parameters. Metaheuristic algorithms have been reported to be the best methods for obtaining accurate values of these intrinsic parameters. However, local convergence goes against the recently devised heuristic methods and inhibits them from producing optimal result. This paper proposes a hybrid method that is based on the Newton Raphson method and a self-adaptive algorithm called the Drone Squadron Optimisation. The latter is an artifact technique inspired by the simula- tion of a drone squadron from a command centre. It is proposed that this hybrid method can help extract the best intrinsic parameters of photovoltaic cell and module. This study also provides insights and clarifcation on the reported approaches that have been recently proposed to formulate the objective function. Further, this study also computes and compares the ten best recently published heuristics algorithms in the domain of photovoltaic estimation. The study’s results obtain point to the diference between the two formulations and the accuracy of the best formulation. The results obtained from the six case studies covered in this study present the combined performance of the Newton Raphson method and Drone Squadron Optimisation to extract the accurate parameters of a photovoltaic module. Keywords Photovoltaic modelling · Parameter identifcation · Solar cells · Photovoltaic modules · Drone Squadron Optimization · Metaheuritic algorithms List of Symbols CBC The current best coordinates CBOFV The current best coordinates objective function D Dimension ε Stopping criteria E Solar irradiance F (θ) Objective function to minimize I Cell output current (A) I d , I d1 , I d2 Diode currents (A) I iext (θ) Estimated current I i Measured current (A) I 0 , I 01 , I 02 Diode reverse saturation currents (μA) I p Current through parallel resistor (A) I ph Photoelectric current (A) I sc Short-circuit current (A) k Boltzman constant (J/K) k i Temperature coefcient of Isc (A/K) LB Lower bound n, n 1 , n 2 Diode ideality factors N Number of the experimental I–V data pairs N S Number of cells connected in series OF Objective function θ Vector of parameters P Firmware perturbation Online ISSN 2092-7592 Print ISSN 1229-7607 * Patrick Juvet Gnetchejo patrijuvet@yahoo.fr 1 Laboratory of Technologies and Applied Sciences, University of Douala, Douala, Cameroon 2 Signal, Image and Systems Laboratory, Higher Technical Teacher Training College of Ebolowa, University of Yaounde 1, Yaoundé, Cameroon 3 School of Geology and Mining Engineering, University of Ngaoundéré, Ngaoundéré, Cameroon 4 Laboratory of Electrical Engineering, Mechatronic and Signal Treatment, National Advanced School of Engineering, University of Yaounde 1, Yaoundé, Cameroon 5 College of Physics and Information Engineering, Fuzhou University, Fuzhou, China